
%run ./data/data.ipynb

Pulling data from sources.json:
(Source: CBS)  deceased @ https://opendata.cbs.nl/statline/portal.html?_catalog=CBS&_la=nl&tableId=70895ned&_theme=76
(Source: CBS)  population @ https://opendata.cbs.nl/statline/portal.html?_catalog=CBS&_la=nl&tableId=37230ned&_theme=76
(Source: RIVM)  counts_per_municipality @ https://data.rivm.nl/geonetwork/srv/dut/catalog.search#/metadata/5f6bc429-1596-490e-8618-1ed8fd768427
(Source: RIVM)  prevalence @ https://data.rivm.nl/geonetwork/srv/dut/catalog.search#/metadata/097155aa-75eb-4caa-8ed3-4c6edb80467e
(Source: RIVM)  r @ https://data.rivm.nl/geonetwork/srv/dut/catalog.search#/metadata/ed0699d1-c9d5-4436-8517-27eb993eab6e
(Source: RIVM)  homes_for_elderly @ https://data.rivm.nl/geonetwork/srv/dut/catalog.search#/metadata/0179dd26-7bf6-4021-857f-8623aaf8e73a
(Source: RIVM)  homes_for_disabled @ https://data.rivm.nl/geonetwork/srv/dut/catalog.search#/metadata/aa28b74d-7150-47bc-a7cb-98876e93381b
(Source: RIVM)  seventy_plus_living_at_home @ https://data.rivm.nl/geonetwork/srv/dut/catalog.search#/metadata/40508e17-7296-4f39-ad25-8ddd0c904087
(Source: RIVM)  sewage @ https://data.rivm.nl/geonetwork/srv/dut/catalog.search#/metadata/a2960b68-9d3f-4dc3-9485-600570cd52b9
(Source: RIVM)  support_and_behavior @ https://data.rivm.nl/geonetwork/srv/dut/catalog.search#/metadata/8a72d78a-fcf8-4882-b0ab-cd594961a267?tab=relations
(Source: RIVM)  wekelijkse_update @ https://www.rivm.nl/coronavirus-covid-19/actueel/wekelijkse-update-epidemiologische-situatie-covid-19-in-nederland
(Source: NICE)  hospital_admission @ https://data.rivm.nl/geonetwork/srv/dut/catalog.search#/metadata/4f4ad069-8f24-4fe8-b2a7-533ef27a899f
(Source: NICE)  ic_admission @ https://data.rivm.nl/geonetwork/srv/dut/catalog.search#/metadata/45f911c4-3a62-42f1-b594-524a75db2c94
(Source: LCPS)  hospital_occupation @ https://lcps.nu/datafeed/
(Source: Nivel)  general_practioner @ https://www.nivel.nl/nl/nivel-zorgregistraties-eerste-lijn/monitor-cijfers-covid-19-achtige-klachten-huisartsenpraktijken#opendata

Creating dataframes for analysis:
dataframe from cbs created, per week per agegroup: deceased_week_agegroup
dataframe from cbs created, per week: deceased_per_week
dataframe from cbs created, per year per agegroup: deceased_year_agegroup
dataframe from cbs created, per year: deceased_per_year
dataframe from cbs created, per month per municipality: population_month_municipality
dataframe from cbs created, per year per municipality: population_year_municipality
dataframe from cbs created, per month: population_per_month
dataframe from cbs created, per month: population_per_year
dataframe for RIVM data created, per day: rivm_per_day
dataframe for RIVM data created: rivm_per_week
dataframe for RIVM data created, per month: rivm_per_month
dataframe for RIVM data created, per month: rivm_per_year
dataframe from cbs created, per week per agegroup: rivm_week_agegroup
dataframe CBS and RIVM combined, per week: per_week
dataframe CBS and RIVM combined, per month: per_month
dataframe CBS and RIVM combined, per month: per_year


import os
import sys
import click
import numpy as np
import seaborn as sns
import datetime

from SEIR.seir import SEIR
from SEIR.visualization import visualize_seir_computation

from matplotlib import rcParams
rcParams['figure.figsize'] = 20,10


visualize_compartments = False
output_file_format = 'csv'
#output_file_format = 'json'
output_file = './data/csvs/seir.'+output_file_format


# Define model
def run_model(action=None):
    model = SEIR(
        incubation_period=incubation_period,
        infectious_period=infectious_period,
        initial_R0=initial_R0,
        hospitalization_probability=hospitalization_probability,
        hospitalization_duration=hospitalization_duration,
        hospitalization_lag_from_onset=hospitalization_lag_from_onset,
        icu_probability=icu_probability,
        icu_duration=icu_duration,
        icu_lag_from_onset=icu_lag_from_onset,
        death_probability=death_probability,
        death_lag_from_onset=death_lag_from_onset,
        population=population,
        compartments = compartments,
        contacts_matrix = contacts_matrix,
        restrictions_function = restrictions_function,
        imported_cases_function = imported_cases_function
    )
    
    model.set_initial_state(
        population_exposed = population_exposed,
        population_infected = population_infected,
        probabilities = probabilities
    )
    
    model.simulate(
        max_simulation_time = max_simulation_time,
        method = method,
        max_step = max_step
    )
    
    # Evaluate the solution
    time = np.arange(0, max_simulation_time, 1, dtype=int)
    global results
    results = model.evaluate_solution(time)
    
    # Visualize the results
    if action == 'show':
        visualize_seir_computation(
            results,
            compartments=compartments,
            restrictions_info=restr_info,
            show_individual_compartments=visualize_compartments
        )
    elif action == 'save':
        if output_file_format == 'csv':
            results.to_csv(output_file)
        elif output_file_format == 'json':
            results.to_json(output_file)
    else:
        return results


# test settings
incubation_period = 3
infectious_period = 7
initial_R0 = 1
hospitalization_probability = 0.1
hospitalization_duration = 20
hospitalization_lag_from_onset = 7
icu_probability = 0.01
icu_duration = 10
icu_lag_from_onset = 11
death_probability = 0.1
death_lag_from_onset = 25
population = 17e6


compartments = None #Optional[List[Any]]
contacts_matrix = None # Optional[numpy.ndarray]
restrictions_function = None # Optional[Callable[[float], Union[float, numpy.ndarray]]]
imported_cases_function = None # Optional[Callable]


restr_info = ''

#[restriction everyone-begin-careful]
#day-begins = 20
#day-ends = 180
#infectivity modifier = 0.7

#[restriction school-closure]
#day-begins = 40
#day-ends = 100
#infectivity modifier = 
#   [5-9, 10-14, 15-19] : [5-9, 10-14, 15-19] : 0.5714
#   all : [65-69, 70+] : 0.7143

#[restriction random-efforts]
#day-begins = 1
#day-ends = 180
#infectivity modifier = file://infectivity_modifier_matrix_example.csv


# Setup initial state
population_exposed = 0.005
population_infected = 0.005
probabilities = True

# Simulate up to 200 days
max_simulation_time = 200
method = 'DOP853'
max_step = 0.5


# https://www.rivm.nl/coronavirus-covid-19/ziekte:
## Als je besmet raakt duurt het meestal 5 tot 6 dagen voordat je klachten krijgt.
incubation_period = 5
# https://amazingerasmusmc.nl/biomedisch/hoelang-is-een-covid-19-patient-besmettelijk:
## We hebben aangetoond dat ernstig zieke covid-19-patienten tot zo’n vijftien dagen infectieus kunnen zijn.
## De meeste gegevens die tot nu toe werden gepubliceerd, zijn gebaseerd op metingen bij patiënten met milde klachten. Dan wordt uitgegaan van een dag of tien.
# https://www.gezondheidenwetenschap.be/gezondheid-in-de-media/wanneer-is-iemand-met-covid-19-het-meest-besmettelijk:
## In geen enkele studie konden onderzoekers nog levend virus aantonen 9 dagen na de start van de symptomen.
infectious_period = 9

initial_R0 = r['Rt_avg'][0]
print('initial_R0 = '+str(initial_R0))

hospitalization_probability = rivm_per_day['Hospital_admission_NICE'].sum()/rivm_per_day['Positive_tests'].sum()
print('hospitalization_probability until now = '+str(round(hospitalization_probability,4)))
icu_probability = rivm_per_day['IC_admission'].sum()/rivm_per_day['Positive_tests'].sum()
print('icu_probability = '+str(round(icu_probability,4)))
death_probability = rivm_per_day['Deceased_covid'].sum()/rivm_per_day['Positive_tests'].sum()
print('death_probability = '+str(round(death_probability,4)))

# https://www.rtlnieuws.nl/economie/artikel/5196644/corona-behandeling-kosten-testen-huisarts-intensive-care-totaal:
## Uit onderzoek van de Nederlandse Internisten Vereniging (NIV) komt naar voren dat een coronapatiënt gemiddeld zeven dagen op de verpleegafdeling ligt en/of dertien dagen op de intensive care.
hospitalization_duration = 7
icu_duration = 13

# https://wibnet.nl/geneeskunde/ziektes/van-dag-tot-dag-zo-neemt-het-coronavirus-je-lichaam-over:
## Na zeven dagen worden patiënten met ernstige klachten opgenomen in het ziekenhuis.
## Als de toestand verslechtert of als er nog meer organen uitvallen, wordt de patiënt na 10 dagen meestal opgenomen op de intensive care.
hospitalization_lag_from_onset = 7
icu_lag_from_onset = 10

# https://www.stichting-nice.nl/covid-19-op-de-ic.jsp:
## Verdeling overleden in ziekenhuis is vlakker dan levend uit ziekenhuis, doet gemiddelde verschuiven. standaard van model is 25 dagen. laat ik zo
death_lag_from_onset = 25

# Setup initial state
ni = 36
e = str(rivm_per_day.index[ni].year * 100 + rivm_per_day.index[ni].month)
e = e[:4] + '-' + e[-2:]
population_exposed = rivm_per_day.loc[rivm_per_day.index[ni],'Positive_tests'].sum() / population_per_month.loc[e,'Population']
population_infected = rivm_per_day.loc[rivm_per_day.index[ni],'prev_avg'].sum() / population_per_month.loc[e,'Population']

initial_R0 = 1.93
hospitalization_probability until now = 0.0292
icu_probability = 0.0055
death_probability = 0.007


run_model('show')


df = pd.DataFrame()
n=0
max_simulation_time = 60
for i in rivm_per_day.index:
    try:
        r['Rt_avg'][i]
    except:
        pass
    else:
        initial_R0 = r['Rt_avg'][i]
        hospitalization_probability = rivm_per_day['Hospital_admission_NICE'][:i].sum()/rivm_per_day['Positive_tests'][:i].sum()
        icu_probability = rivm_per_day.loc[:i,'IC_admission'].sum()/rivm_per_day.loc[:i,'Positive_tests'].sum()
        death_probability = rivm_per_day.loc[:i,'Deceased_covid'].sum()/rivm_per_day.loc[:i,'Positive_tests'].sum()
        p = str(rivm_per_day.index[ni].year * 100 + rivm_per_day.index[ni].month)
        p = e[:4] + '-' + e[-2:]
        population = population_per_month.loc[p,'Population']
        # Setup initial state
        population_exposed = ( rivm_per_day.loc[i + pd.Timedelta(days=-7):i,'Positive_tests'].sum() / 7) / population
        population_infected = ( rivm_per_day.loc[i + pd.Timedelta(days=-7):i,'prev_avg'].sum() / 7) / population
        probabilities = True

        run_model()
        results['time'] = results[ 'time'].astype(int)
        results['i'] = i
        results['time'] = results.apply(lambda x: x['i'] + pd.Timedelta(days=x['time']), axis=1)
        results['time'] = results['time'].astype(str)
        n+=1
        df = df.append(results)
df.reset_index(inplace=True, drop=True)
df

/tmp/ipykernel_29887/3497095173.py:11: RuntimeWarning: invalid value encountered in double_scalars
  hospitalization_probability = rivm_per_day['Hospital_admission_NICE'][:i].sum()/rivm_per_day['Positive_tests'][:i].sum()
/tmp/ipykernel_29887/3497095173.py:12: RuntimeWarning: invalid value encountered in double_scalars
  icu_probability = rivm_per_day.loc[:i,'IC_admission'].sum()/rivm_per_day.loc[:i,'Positive_tests'].sum()
/tmp/ipykernel_29887/3497095173.py:13: RuntimeWarning: invalid value encountered in double_scalars
  death_probability = rivm_per_day.loc[:i,'Deceased_covid'].sum()/rivm_per_day.loc[:i,'Positive_tests'].sum()
/tmp/ipykernel_29887/3497095173.py:11: RuntimeWarning: divide by zero encountered in double_scalars
  hospitalization_probability = rivm_per_day['Hospital_admission_NICE'][:i].sum()/rivm_per_day['Positive_tests'][:i].sum()
/home/cjvanzwol/miniconda3/envs/covid/lib/python3.9/site-packages/SEIR/seir.py:507: RuntimeWarning: invalid value encountered in multiply
  np.multiply(self.hospitalization_probability, np.divide(Ehl, self.incubation_period))


#rcParams['figure.figsize'] = 20,100
#df.plot(subplots=True,kind='bar')


sns.relplot(
    data=df,
    x="time", y="susceptible",
    hue="i",
    kind="line",
    height=10, aspect=1.5, 
    facet_kws=dict(sharex=False),
    legend=False
)

<seaborn.axisgrid.FacetGrid at 0x7b33e4852730>


sns.relplot(
    data=df,
    x="time", y="exposed",
    hue="i",
    kind="line",
    height=10, aspect=1.5, 
    facet_kws=dict(sharex=False),
    legend=False
)

<seaborn.axisgrid.FacetGrid at 0x7b33d6eb4fd0>


rcParams['figure.figsize'] = 20,10
rivm_per_day[['Positive_tests','prev_avg']].plot()

<AxesSubplot:xlabel='Date'>


sns.relplot(
    data=df,
    x="time", y="infected (active)",
    hue="i",
    kind="line",
    height=10, aspect=1.5, 
    facet_kws=dict(sharex=False),
    legend=False
)

<seaborn.axisgrid.FacetGrid at 0x7b33d5e36610>


sns.relplot(
    data=df,
    x="time", y="infected (total)",
    hue="i",
    kind="line",
    height=10, aspect=1.5, 
    facet_kws=dict(sharex=False),
    legend=False
)

<seaborn.axisgrid.FacetGrid at 0x7b33d54cc040>


sns.relplot(
    data=df,
    x="time", y="removed",
    hue="i",
    kind="line",
    height=10, aspect=1.5, 
    facet_kws=dict(sharex=False),
    legend=False
)

<seaborn.axisgrid.FacetGrid at 0x7b33cfdfae20>


sns.relplot(
    data=df,
    x="time", y="exposed",
    hue="i",
    kind="line",
    height=10, aspect=1.5, 
    facet_kws=dict(sharex=False),
    legend=False
)

<seaborn.axisgrid.FacetGrid at 0x7b33cf490f40>


sns.relplot(
    data=df,
    x="time", y="hospitalized (active)",
    hue="i",
    kind="line",
    height=10, aspect=1.5, 
    facet_kws=dict(sharex=False),
    legend=False
)

<seaborn.axisgrid.FacetGrid at 0x7b33ce1d3910>


rivm_per_day[['Hospital_admission','Hospital_admission_NICE']].plot()

<AxesSubplot:xlabel='Date'>


sns.relplot(
    data=df,
    x="time", y="in ICU",
    hue="i",
    kind="line",
    height=10, aspect=1.5, 
    facet_kws=dict(sharex=False),
    legend=False
)

<seaborn.axisgrid.FacetGrid at 0x7b33ccf0d7f0>


rivm_per_day['IC_Bedden_COVID'].plot()

<AxesSubplot:xlabel='Date'>


sns.relplot(
    data=df,
    x="time", y="deaths",
    hue="i",
    kind="line",
    height=10, aspect=1.5, 
    facet_kws=dict(sharex=False),
    legend=False
)

<seaborn.axisgrid.FacetGrid at 0x7b33cbef76d0>


rivm_per_day['Deceased_covid'].plot()

<AxesSubplot:xlabel='Date'>


sns.set_theme(style="ticks")

for c in df.columns:
    if c.__contains__('All') or c.__contains__('time') or c.__contains__('i'):
        pass
    else:
        # Plot the lines on two facets
        sns.relplot(
            data=df,
            x="time", y=c,
            hue="i",
            kind="line",
            height=10, aspect=1.5, 
            facet_kws=dict(sharex=False),
            legend=False
        )


df2 = pd.DataFrame()
n=0
max_simulation_time = 60
for i in rivm_per_day.index:
    try:
        r['Rt_avg'][i]
    except:
        pass
    else:
        f = i + pd.Timedelta(days=-max_simulation_time)
        initial_R0 = r['Rt_avg'][i]
        hospitalization_probability = rivm_per_day['Hospital_admission_NICE'][:i].sum()/rivm_per_day['Positive_tests'][:i].sum()
        icu_probability = rivm_per_day['IC_admission'][f:i].sum()/rivm_per_day['Positive_tests'][:i].sum()
        death_probability = rivm_per_day['Deceased_covid'][f:i].sum()/rivm_per_day['Positive_tests'][:i].sum()
        p = str(rivm_per_day.index[ni].year * 100 + rivm_per_day.index[ni].month)
        p = e[:4] + '-' + e[-2:]
        population = population_per_month.loc[p,'Population']
        # Setup initial state
        population_exposed = rivm_per_day.loc[i + pd.Timedelta(days=-7):i,'Positive_tests'].sum() / population
        population_infected = rivm_per_day.loc[i + pd.Timedelta(days=-7):i,'prev_avg'].sum() / population
        probabilities = True

        run_model()
        results['time'] = results[ 'time'].astype(int)
        results['i'] = i
        results['time'] = results.apply(lambda x: x['i'] + pd.Timedelta(days=x['time']), axis=1)
        results['time'] = results['time'].astype(str)
        n+=1
        df2 = df2.append(results)
df2.reset_index(inplace=True, drop=True)
df2

/tmp/ipykernel_29887/239506404.py:12: RuntimeWarning: invalid value encountered in double_scalars
  hospitalization_probability = rivm_per_day['Hospital_admission_NICE'][:i].sum()/rivm_per_day['Positive_tests'][:i].sum()
/tmp/ipykernel_29887/239506404.py:13: RuntimeWarning: invalid value encountered in double_scalars
  icu_probability = rivm_per_day['IC_admission'][f:i].sum()/rivm_per_day['Positive_tests'][:i].sum()
/tmp/ipykernel_29887/239506404.py:14: RuntimeWarning: invalid value encountered in double_scalars
  death_probability = rivm_per_day['Deceased_covid'][f:i].sum()/rivm_per_day['Positive_tests'][:i].sum()
/tmp/ipykernel_29887/239506404.py:12: RuntimeWarning: divide by zero encountered in double_scalars
  hospitalization_probability = rivm_per_day['Hospital_admission_NICE'][:i].sum()/rivm_per_day['Positive_tests'][:i].sum()
/home/cjvanzwol/miniconda3/envs/covid/lib/python3.9/site-packages/SEIR/seir.py:507: RuntimeWarning: invalid value encountered in multiply
  np.multiply(self.hospitalization_probability, np.divide(Ehl, self.incubation_period))


for c in df.columns:
    if c.__contains__('All') or c.__contains__('time') or c.__contains__('i'):
        pass
    else:
        df[c].plot()


sns.relplot(
    data=df2,
    x="time", y="exposed",
    hue="i",
    kind="line",
    height=10, aspect=1.5, 
    facet_kws=dict(sharex=False),
    legend=False
)

<seaborn.axisgrid.FacetGrid at 0x7b33cb29f2b0>


for c in df2.columns:
    if c.__contains__('All') or c.__contains__('time'):
        pass
    else:
        # Plot the lines on two facets
        sns.relplot(
            data=df2,
            x="time", y=c,
            hue="i",
            kind="line",
            height=10, aspect=1.5, 
            facet_kws=dict(sharex=False),
            legend=False
        )

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
/tmp/ipykernel_29887/2407878476.py in <module>
      4     else:
      5         # Plot the lines on two facets
----> 6         sns.relplot(
      7             data=df2,
      8             x="time", y=c,

~/miniconda3/envs/covid/lib/python3.9/site-packages/seaborn/_decorators.py in inner_f(*args, **kwargs)
     44             )
     45         kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
---> 46         return f(**kwargs)
     47     return inner_f
     48 

~/miniconda3/envs/covid/lib/python3.9/site-packages/seaborn/relational.py in relplot(x, y, hue, size, style, data, row, col, col_wrap, row_order, col_order, palette, hue_order, hue_norm, sizes, size_order, size_norm, markers, dashes, style_order, legend, kind, height, aspect, facet_kws, units, **kwargs)
   1021 
   1022     # Draw the plot
-> 1023     g.map_dataframe(func, **plot_kws)
   1024 
   1025     # Label the axes

~/miniconda3/envs/covid/lib/python3.9/site-packages/seaborn/axisgrid.py in map_dataframe(self, func, *args, **kwargs)
    748 
    749             # Draw the plot
--> 750             self._facet_plot(func, ax, args, kwargs)
    751 
    752         # Finalize the annotations and layout

~/miniconda3/envs/covid/lib/python3.9/site-packages/seaborn/axisgrid.py in _facet_plot(self, func, ax, plot_args, plot_kwargs)
    773             plot_args = []
    774             plot_kwargs["ax"] = ax
--> 775         func(*plot_args, **plot_kwargs)
    776 
    777         # Sort out the supporting information

~/miniconda3/envs/covid/lib/python3.9/site-packages/seaborn/_decorators.py in inner_f(*args, **kwargs)
     44             )
     45         kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
---> 46         return f(**kwargs)
     47     return inner_f
     48 

~/miniconda3/envs/covid/lib/python3.9/site-packages/seaborn/relational.py in lineplot(x, y, hue, size, style, data, palette, hue_order, hue_norm, sizes, size_order, size_norm, dashes, markers, style_order, units, estimator, ci, n_boot, seed, sort, err_style, err_kws, legend, ax, **kwargs)
    701     p._attach(ax)
    702 
--> 703     p.plot(ax, kwargs)
    704     return ax
    705 

~/miniconda3/envs/covid/lib/python3.9/site-packages/seaborn/relational.py in plot(self, ax, kws)
    527                     ax.plot(x[rows], y[rows], **kws)
    528             else:
--> 529                 line, = ax.plot(x, y, **kws)
    530 
    531             # --- Draw the confidence intervals

~/miniconda3/envs/covid/lib/python3.9/site-packages/matplotlib/axes/_axes.py in plot(self, scalex, scaley, data, *args, **kwargs)
   1605         lines = [*self._get_lines(*args, data=data, **kwargs)]
   1606         for line in lines:
-> 1607             self.add_line(line)
   1608         self._request_autoscale_view(scalex=scalex, scaley=scaley)
   1609         return lines

~/miniconda3/envs/covid/lib/python3.9/site-packages/matplotlib/axes/_base.py in add_line(self, line)
   2099             line.set_clip_path(self.patch)
   2100 
-> 2101         self._update_line_limits(line)
   2102         if not line.get_label():
   2103             line.set_label('_line%d' % len(self.lines))

~/miniconda3/envs/covid/lib/python3.9/site-packages/matplotlib/axes/_base.py in _update_line_limits(self, line)
   2121         Figures out the data limit of the given line, updating self.dataLim.
   2122         """
-> 2123         path = line.get_path()
   2124         if path.vertices.size == 0:
   2125             return

~/miniconda3/envs/covid/lib/python3.9/site-packages/matplotlib/lines.py in get_path(self)
   1020         """
   1021         if self._invalidy or self._invalidx:
-> 1022             self.recache()
   1023         return self._path
   1024 

~/miniconda3/envs/covid/lib/python3.9/site-packages/matplotlib/lines.py in recache(self, always)
    666         if always or self._invalidy:
    667             yconv = self.convert_yunits(self._yorig)
--> 668             y = _to_unmasked_float_array(yconv).ravel()
    669         else:
    670             y = self._y

~/miniconda3/envs/covid/lib/python3.9/site-packages/matplotlib/cbook/__init__.py in _to_unmasked_float_array(x)
   1331         return np.ma.asarray(x, float).filled(np.nan)
   1332     else:
-> 1333         return np.asarray(x, float)
   1334 
   1335 

TypeError: float() argument must be a string or a number, not 'Period'


import epintervene

	time	(susceptible, All)	susceptible	(exposed, All)	exposed	(infected (active), All)	infected (active)	(infected (total), All)	infected (total)	(removed, All)	removed	(hospitalized (active), All)	hospitalized (active)	(in ICU, All)	in ICU	(deaths, All)	deaths	i
0	2020-02-17	1.742410e+07	1.742410e+07	0.000000	0.000000	153.428571	153.428571	153.428571	153.428571	0.000000	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	2020-02-17
1	2020-02-18	1.742407e+07	1.742407e+07	28.372813	28.372813	140.162923	140.162923	156.265853	156.265853	16.244591	16.244591	NaN	NaN	NaN	NaN	NaN	NaN	2020-02-17
2	2020-02-19	1.742404e+07	1.742404e+07	49.664394	49.664394	132.937719	132.937719	164.069573	164.069573	31.370085	31.370085	NaN	NaN	NaN	NaN	NaN	NaN	2020-02-17
3	2020-02-20	1.742401e+07	1.742401e+07	66.146833	66.146833	130.009387	130.009387	175.650696	175.650696	45.944790	45.944790	NaN	NaN	NaN	NaN	NaN	NaN	2020-02-17
4	2020-02-21	1.742399e+07	1.742399e+07	79.399175	79.399175	130.176979	130.176979	190.205297	190.205297	60.375267	60.375267	NaN	NaN	NaN	NaN	NaN	NaN	2020-02-17
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
39115	2022-01-23	1.718872e+07	1.718872e+07	0.426478	0.426478	592.290659	592.290659	235626.443608	235626.443608	234949.094394	234949.094394	0.081087	0.081087	29.810434	29.810434	188.690834	188.690834	2021-11-29
39116	2022-01-24	1.718872e+07	1.718872e+07	0.349171	0.349171	530.078000	530.078000	235626.521173	235626.521173	235011.384360	235011.384360	0.066389	0.066389	26.827038	26.827038	188.775828	188.775828	2021-11-29
39117	2022-01-25	1.718872e+07	1.718872e+07	0.285877	0.285877	474.394429	474.394429	235626.584678	235626.584678	235067.131225	235067.131225	0.055227	0.055227	24.155482	24.155482	188.845415	188.845415	2021-11-29
39118	2022-01-26	1.718872e+07	1.718872e+07	0.234056	0.234056	424.555742	424.555742	235626.636671	235626.636671	235117.021733	235117.021733	0.046088	0.046088	21.763317	21.763317	188.902388	188.902388	2021-11-29
39119	2022-01-27	1.718872e+07	1.718872e+07	0.191629	0.191629	379.949250	379.949250	235626.679239	235626.679239	235161.670651	235161.670651	0.038606	0.038606	19.621442	19.621442	188.949033	188.949033	2021-11-29

	time	(susceptible, All)	susceptible	(exposed, All)	exposed	(infected (active), All)	infected (active)	(infected (total), All)	infected (total)	(removed, All)	removed	(hospitalized (active), All)	hospitalized (active)	(in ICU, All)	in ICU	(deaths, All)	deaths	i
0	2020-02-17	1.742318e+07	1.742318e+07	0.000000	0.000000	1074.000000	1074.000000	1.074000e+03	1.074000e+03	0.000000e+00	0.000000e+00	NaN	NaN	NaN	NaN	NaN	NaN	2020-02-17
1	2020-02-18	1.742296e+07	1.742296e+07	198.598014	198.598014	981.139323	981.139323	1.093860e+03	1.093860e+03	1.137121e+02	1.137121e+02	NaN	NaN	NaN	NaN	NaN	NaN	2020-02-17
2	2020-02-19	1.742276e+07	1.742276e+07	347.627948	347.627948	930.559724	930.559724	1.148482e+03	1.148482e+03	2.195903e+02	2.195903e+02	NaN	NaN	NaN	NaN	NaN	NaN	2020-02-17
3	2020-02-20	1.742256e+07	1.742256e+07	462.993813	462.993813	910.056466	910.056466	1.229545e+03	1.229545e+03	3.216125e+02	3.216125e+02	NaN	NaN	NaN	NaN	NaN	NaN	2020-02-17
4	2020-02-21	1.742237e+07	1.742237e+07	555.748336	555.748336	911.223013	911.223013	1.331419e+03	1.331419e+03	4.226244e+02	4.226244e+02	NaN	NaN	NaN	NaN	NaN	NaN	2020-02-17
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
39115	2022-01-23	1.577546e+07	1.577546e+07	2.985346	2.985346	4146.034611	4146.034611	1.649385e+06	1.649385e+06	1.644644e+06	1.644644e+06	0.567611	0.567611	21.989349	21.989349	80.619522	80.619522	2021-11-29
39116	2022-01-24	1.577546e+07	1.577546e+07	2.444194	2.444194	3710.546002	3710.546002	1.649386e+06	1.649386e+06	1.645080e+06	1.645080e+06	0.464721	0.464721	19.788679	19.788679	80.655836	80.655836	2021-11-29
39117	2022-01-25	1.577546e+07	1.577546e+07	2.001137	2.001137	3320.761004	3320.761004	1.649386e+06	1.649386e+06	1.645470e+06	1.645470e+06	0.386586	0.386586	17.818034	17.818034	80.685567	80.685567	2021-11-29
39118	2022-01-26	1.577546e+07	1.577546e+07	1.638392	1.638392	2971.890191	2971.890191	1.649386e+06	1.649386e+06	1.645819e+06	1.645819e+06	0.322614	0.322614	16.053479	16.053479	80.709909	80.709909	2021-11-29
39119	2022-01-27	1.577546e+07	1.577546e+07	1.341402	1.341402	2659.644753	2659.644753	1.649387e+06	1.649387e+06	1.646132e+06	1.646132e+06	0.270239	0.270239	14.473548	14.473548	80.729839	80.729839	2021-11-29

Check RIVM modellen¶

Tried covid-seird = False¶

trying SEIR¶

Setup the model¶

Execute model¶

trying epintervene¶